Reward system related to a vehicle-to-vehicle communication system

ABSTRACT

System and methods are disclosed for determining, through vehicle-to-vehicle communication, whether vehicles are involved in autonomous droning. Vehicle driving data and other information may be used to calculate an autonomous droning reward amount. In addition, vehicle involved in a drafting relationship in addition to, or apart from, an autonomous droning relationship may be financially rewarded. Moreover, aspects of the disclosure related to determining ruminative rewards and/or aspects of vehicle insurance procurement/underwriting.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/163,719, filed Jan. 24, 2014, now U.S. Pat. No. 10,096,067 andentitled “Reward System Related to a Vehicle-to-Vehicle CommunicationSystem,” which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

Aspects of the disclosure generally relate to the analysis of vehicledriving data, including vehicle driving data from vehicles involved inaspects of autonomous driving. In particular, various aspects of thedisclosure relate to receiving and transmitting driving data, includeddata related to autonomous driving relationships between vehicles,analyzing driving data, and determining ruminative rewards and/oraspects of vehicle insurance procurement.

BACKGROUND

Many vehicles include sensors and internal computer systems designed tomonitor and control vehicle operations, driving conditions, and drivingfunctions. Advanced vehicles systems can perform such tasks asmonitoring fuel consumption and optimizing engine operation to achievehigher fuel efficiency, detecting and correcting a loss of traction onan icy road, and detecting a collision and automatically contactingemergency services. Various vehicle-based communication systems allowvehicles to communicate with other devices inside or outside of thevehicle. For example, a Bluetooth system may enable communicationbetween the vehicle and the driver's mobile phone. Telematics systemsmay be configured to access vehicle computers and sensor data, includingon-board diagnostics systems (OBD), and transmit the data to a displaywithin the vehicle, a personal computer or mobile device, or to acentralized data processing system. Data obtained from vehicle sensorsand OBD systems has been used for a variety of purposes, includingmaintenance, diagnosis, and analysis. Additionally, vehicle-to-vehicle(V2V) communication systems can be used to provide drivers with safetywarnings and collision alerts based on data received from other nearbyvehicles. Additionally, vehicles can include autonomous driving systemsthat assume all or part of real-time driving functions to operate thevehicle without real-time input from a human operator.

When out on the road, vehicles and drivers may engage in many differenttypes of driving behaviors, including various “social interactions” withother vehicles and drivers. An example social interaction can includevehicle drafting where one vehicle follows another vehicle to reduce theoverall effect of drag and improve fuel efficiency. Another examplesocial interaction can include autonomous vehicle droning where avehicle engages in at least partial autonomous driving based on thedriving of a lead or pilot vehicle.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to methods and computer devices forreceiving and transmitting driving data, analyzing driving data, anddetermining whether a first vehicle is engaged in a draftingrelationship with at least a second vehicle and allocating a draftingreward based on the drafting relationship. A drafting characteristic ofthe drafting relationship can be determined using vehicle operationaldata. Example drafting characteristics can include a vehicle spacing,vehicle speed, and/or vehicle type. A drafting property associated withthe first or second vehicle can be determined using the draftingcharacteristic. Example drafting properties can include a drafting fuelsavings rate, a drafting savings amount, and/or a percentage increase inmiles-per-gallon. Example drafting rewards can include a cash payment, acarbon credit, a fuel credit, a tax credit, a rebate, and at least aportion of a drafting fuel savings amount associated with the firstvehicle driving analysis computer.

In accordance with further aspects of the present disclosure, a propertyof an insurance policy may be determined by receiving and analyzingdriving data from vehicles engaged in an autonomous droning relationshipwhere a vehicle engages in at least partial autonomous driving based oninformation from another vehicle. A characteristic of the autonomousdroning relationship can be determined from, for example, the receiveddriving data. Example characteristics of an autonomous droningrelationship include identification of a lead vehicle and a dronevehicle, an amount of time a vehicle is the lead or drone vehicle, andthe amount of driving distance a vehicle is the lead or drone vehicle.Example properties of an insurance policy can include a premium for thefirst insurance policy, a deductible of the first insurance policy, anda coverage amount of the first insurance policy. Aspects of thedisclosure also include determining an autonomous droning insurancefactor using vehicle operational data and/or a characteristic of theautonomous droning relationship. The autonomous droning insurance factorcan be used to determine the property of the first insurance policyusing the autonomous droning insurance factor.

Aspects of the present disclosure provide incentives for beneficialsocial interactions between vehicles and drivers. Other features andadvantages of the disclosure will be apparent from the additionaldescription provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

FIG. 2 is a diagram illustrating various example components of a drivinganalysis computing device according to one or more aspects of thedisclosure.

FIG. 3 is a flow diagram illustrating an example method of analyzingvehicle driving data, including determining whether vehicles are engagedin a drafting relationship, determining a drafting property associatedwith a vehicle in a drafting relationship, and allocating a draftingreward based on the drafting property.

FIG. 4 is a flow diagram illustrating an example method of analyzingvehicle driving data, including receiving vehicle operational datapertaining to vehicles engaged in an autonomous droning relationship,determining a characteristic of an autonomous droning relationshipbetween vehicles, and determining a property of an insurance policyusing the characteristic of the autonomous droning relationship.

FIG. 5 is a flow diagram illustrating an example method used in anautonomous droning reward system.

FIGS. 6A-6B are diagrams illustrating examples of various drivinginteractions that may be analyzed according to one or more aspects ofthe disclosure.

FIG. 7 is a diagram illustrating one example of an autonomous droningreward calculator in accordance with various aspects of the disclosure.

FIG. 8 illustrates graphical representations of illustrative formulasfor calculating an autonomous droning reward amount based on one or moreinputs, in accordance with various aspects of the disclosure.

FIG. 9 is a diagram illustrating one example of an account processor inaccordance with various aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a computer system, or a computer program product.Accordingly, those aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment combiningsoftware and hardware aspects. In addition, aspects may take the form ofa computing device configured to perform specified actions. Furthermore,such aspects may take the form of a computer program product stored byone or more computer-readable storage media having computer-readableprogram code, or instructions, embodied in or on the storage media. Anysuitable computer readable storage media may be utilized, including harddisks, CD-ROMs, optical storage devices, magnetic storage devices,and/or any combination thereof. In addition, various signalsrepresenting data or events as described herein may be transferredbetween a source and a destination in the form of electromagnetic wavestraveling through signal-conducting media such as metal wires, opticalfibers, and/or wireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a computing device 101 in drivinganalysis communication system 100 that may be used according to one ormore illustrative embodiments of the disclosure. The driving analysisdevice 101 may have a processor 103 for controlling overall operation ofthe device 101 and its associated components, including RAM 105, ROM107, input/output module 109, and memory unit 115. The computing device101, along with one or more additional devices (e.g., terminals 141,151) may correspond to any of multiple systems or devices, such as adriving analysis computing devices or systems, configured as describedherein for transmitting and receiving vehicle operational data,analyzing vehicle operational data, and determining drivingcharacteristics and various properties related to driver rewards and/orvehicle insurance based on the data. Vehicle operational data caninclude data collected from vehicle sensors and OBD systems. Vehicleoperations can also include data pertaining to the driver of a vehicle.Vehicle operational data can also include data pertaining to othernearby vehicles collected via, for example, V2V communications. As usedherein, vehicle operation data is used interchangeably with drivingdata.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 101 mayprovide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual and/or graphical output. Software may be stored withinmemory unit 115 and/or other storage to provide instructions toprocessor 103 for enabling device 101 to perform various functions. Forexample, memory unit 115 may store software used by the device 101, suchas an operating system 117, application programs 119, and an associatedinternal database 121. The memory unit 115 includes one or more ofvolatile and/or non-volatile computer memory to storecomputer-executable instructions, data, and/or other information.Processor 103 and its associated components may allow the drivinganalysis system 101 to execute a series of computer-readableinstructions to transmit or receive vehicle driving data, analyzedriving data, determine driving characteristics from the driving data,and determine properties of, for example, driver rewards or insurancepolicies based on the driving data.

The driving analysis computing device 101 may operate in a networkedenvironment 100 supporting connections to one or more remote computers,such as terminals/devices 141 and 151. Driving analysis computing device101, and related terminals/devices 141 and 151, may include devicesinstalled in vehicles, mobile devices that may travel within vehicles,or devices outside of vehicles that are configured to receive andprocess vehicle and driving data. Thus, the driving analysis computingdevice 101 and terminals/devices 141 and 151 may each include personalcomputers (e.g., laptop, desktop, or tablet computers), servers (e.g.,web servers, database servers), vehicle-based devices (e.g., on-boardvehicle computers, short-range vehicle communication systems, telematicsdevices), or mobile communication devices (e.g., mobile phones, portablecomputing devices, and the like), and may include some or all of theelements described above with respect to the driving analysis computingdevice 101. The network connections depicted in FIG. 1 include a localarea network (LAN) 125 and a wide area network (WAN) 129, and a wirelesstelecommunications network 133, but may also include other networks.When used in a LAN networking environment, the driving analysiscomputing device 101 may be connected to the LAN 125 through a networkinterface or adapter 123. When used in a WAN networking environment, thedevice 101 may include a modem 127 or other means for establishingcommunications over the WAN 129, such as network 131 (e.g., theInternet). When used in a wireless telecommunications network 133, thedevice 101 may include one or more transceivers, digital signalprocessors, and additional circuitry and software for communicating withwireless computing devices 141 (e.g., mobile phones, short-range vehiclecommunication systems, vehicle telematics devices) via one or morenetwork devices 135 (e.g., base transceiver stations) in the wirelessnetwork 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computing devices and drivinganalysis system components described herein may be configured tocommunicate using any of these network protocols or technologies.

Additionally, one or more application programs 119 used by the drivinganalysis computing device 101 may include computer executableinstructions (e.g., driving data analysis programs, drivingcharacteristic algorithms, driving and insurance policy propertiesalgorithms, and driver reward algorithms) for transmitting and receivingvehicle driving data, determining driving characteristics, anddetermining various properties associated with one or more vehicles ordrivers, and performing other related functions as described herein.

As used herein, a driving characteristic may refer to one or moreactions or events performed by a vehicle and can include aspects ofinformation identified or determined from vehicle operational datacollected from a vehicle. A driving characteristic can include, forexample, a vehicle speed and/or gas mileage identified from the vehicleoperational data. In addition, for example, a driving characteristic mayinclude a vehicle speed or a gas mileage determined from otheroperational data collected from the vehicle. As discussed below, adriving characteristic may be determined from driving data collected bya vehicle sensors and telematics device, and/or additional data receivedfrom other nearby vehicles using vehicle-to-vehicle (V2V)communications. It should be understood that a driving characteristicmay be associated with a vehicle, a driver, or a group of vehicles ordrivers engaged in social interaction, such as an autonomous droningrelationship

FIG. 2 is a diagram of an illustrative driving analysis system 200including two vehicles 210 and 220, a driving analysis server 250, andadditional related components. Each component shown in FIG. 2 may beimplemented in hardware, software, or a combination of the two.Additionally, each component of the driving analysis system 200 mayinclude a computing device (or system) having some or all of thestructural components described above for computing device 101.

Vehicles 210 and 220 in the driving analysis system 200 may be, forexample, automobiles, motorcycles, scooters, buses, recreationalvehicles, boats, or other vehicles for which a vehicle driving data maybe collected and analyzed. The vehicles 210 and 220 each include vehicleoperation sensors 211 and 221 capable of detecting and recording variousconditions at the vehicle and operational parameters of the vehicle. Forexample, sensors 211 and 221 may detect and store data corresponding tothe vehicle's location (e.g., GPS coordinates), speed and direction,rates of acceleration or braking, gas mileage, and specific instances ofsudden acceleration, braking, and swerving. Sensors 211 and 221 also maydetect and store data received from the vehicle's 210 internal systems,such as impact to the body of the vehicle, air bag deployment,headlights usage, brake light operation, door opening and closing, doorlocking and unlocking, cruise control usage, hazard lights usage,windshield wiper usage, horn usage, turn signal usage, seat belt usage,phone and radio usage within the vehicle, maintenance performed on thevehicle, and other data collected by the vehicle's computer systems,including the vehicle OBD.

Additional sensors 211 and 221 may detect and store the external drivingconditions, for example, external temperature, rain, snow, light levels,and sun position for driver visibility. For example, external camerasand proximity sensors 211 and 221 may detect other nearby vehicles,vehicle spacing, traffic levels, road conditions, traffic obstructions,animals, cyclists, pedestrians, and other conditions that may factorinto a driving event data analysis. Sensors 211 and 221 also may detectand store data relating to moving violations and the observance oftraffic signals and signs by the vehicles 210 and 220. Additionalsensors 211 and 221 may detect and store data relating to themaintenance of the vehicles 210 and 220, such as the engine status, oillevel, engine coolant temperature, odometer reading, the level of fuelin the fuel tank, engine revolutions per minute (RPMs), and/or tirepressure.

Vehicles sensors 211 and 221 also may include cameras and/or proximitysensors capable of recording additional conditions inside or outside ofthe vehicles 210 and 220. For example, internal cameras may detectconditions such as the number of the passengers and the types ofpassengers (e.g. adults, children, teenagers, pets, etc.) in thevehicles, and potential sources of driver distraction within the vehicle(e.g., pets, phone usage, and unsecured objects in the vehicle). Sensors211 and 221 also may be configured to collect data a driver's movementsor the condition of a driver. For example, vehicles 210 and 220 mayinclude sensors that monitor a driver's movements, such as the driver'seye position and/or head position, etc. Additional sensors 211 and 221may collect data regarding the physical or mental state of the driver,such as fatigue or intoxication. The condition of the driver may bedetermined through the movements of the driver or through other sensors,for example, sensors that detect the content of alcohol in the air orblood alcohol content of the driver, such as a breathalyzer.

Certain vehicle sensors 211 and 221 also may collect informationregarding the driver's route choice, whether the driver follows a givenroute, and to classify the type of trip (e.g. commute, errand, newroute, etc.). In certain embodiments, sensors and/or cameras 211 and 221may determine when and how often the vehicles 210 and 220 stay in asingle lane or stray into other lanes. A Global Positioning System(GPS), locational sensors positioned inside the vehicles 210 and 220,and/or locational sensors or devices external to the vehicles 210 and220 may be used determine the route, lane position, and other vehicleposition/location data.

The data collected by vehicle sensors 211 and 221 may be stored and/oranalyzed within the respective vehicles 210 and 220, such as for examplea driving analysis computer 217, 227 integrated into the vehicle, and/ormay be transmitted to one or more external devices. For example, asshown in FIG. 2, sensor data may be transmitted via short-rangecommunication systems 212 and 222 to other nearby vehicles.Additionally, the sensor data may be transmitted via telematics devices213 and 223 to one or more remote computing devices, such as drivinganalysis server 250.

Short-range communication systems 212 and 222 are vehicle-based datatransmission systems configured to transmit vehicle operational data toother nearby vehicles, and to receive vehicle operational data fromother nearby vehicles. In some examples, communication systems 212 and222 may use the dedicated short-range communications (DSRC) protocolsand standards to perform wireless communications between vehicles. Inthe United States, 75 MHz in the 5.850-5.925 GHz band have beenallocated for DSRC systems and applications, and various other DSRCallocations have been defined in other countries and jurisdictions.However, short-range communication systems 212 and 222 need not useDSRC, and may be implemented using other short-range wireless protocolsin other examples, such as WLAN communication protocols (e.g., IEEE802.11), Bluetooth (e.g., IEEE 802.15.1), or one or more of theCommunication Access for Land Mobiles (CALM) wireless communicationprotocols and air interfaces. The vehicle-to-vehicle (V2V) transmissionsbetween the short-range communication systems 212 and 222 may be sentvia DSRC, Bluetooth, satellite, GSM infrared, IEEE 802.11, WiMAX, RFID,and/or any suitable wireless communication media, standards, andprotocols. In certain systems, short-range communication systems 212 and222 may include specialized hardware installed in vehicles 210 and 220(e.g., transceivers, antennas, etc.), while in other examples thecommunication systems 212 and 222 may be implemented using existingvehicle hardware components (e.g., radio and satellite equipment,navigation computers) or may be implemented by software running on themobile devices 215 and 225 of drivers and passengers within the vehicles210 and 220.

The range of V2V communications between vehicle communication systems212 and 222 may depend on the wireless communication standards andprotocols used, the transmission/reception hardware (e.g., transceivers,power sources, antennas), and other factors. Short-range V2Vcommunications may range from just a few feet to many miles, anddifferent types of driving behaviors may be determined depending on therange of the V2V communications. For example, V2V communications rangingonly a few feet may be sufficient for a driving analysis computingdevice 101 in one vehicle to determine that another vehicle istailgating or cut-off the vehicle, whereas longer communications mayallow the device 101 to determine additional types of driving behaviors(e.g., vehicle spacing, yielding, defensive avoidance, proper responseto a safety hazard, etc.).

V2V communications also may include vehicle-to-infrastructure (V2I)communications, such as transmissions from vehicles to non-vehiclereceiving devices, for example, toll booths, rail road crossings, androad-side traffic monitoring devices. Certain V2V communication systemsmay periodically broadcast data from a vehicle 210 to any other vehicle,or other infrastructure device capable of receiving the communication,within the range of the vehicle's transmission capabilities. Forexample, a vehicle 210 may periodically broadcast (e.g., every 0.1second, every 0.5 seconds, every second, every 5 seconds, etc.) certainvehicle operation data via its short-range communication system 212,regardless of whether or not any other vehicles or reception devices arein range. In other examples, a vehicle communication system 212 mayfirst detect nearby vehicles and receiving devices, and may initializecommunication with each by performing a handshaking transaction beforebeginning to transmit its vehicle operation data to the other vehiclesand/or devices.

The types of vehicle operational data, or vehicle driving data,transmitted by vehicles 210 and 220 may depend on the protocols andstandards used for the V2V communication, the range of communications,the autonomous driving system, and other factors. In certain examples,vehicles 210 and 220 may periodically broadcast corresponding sets ofsimilar vehicle driving data, such as the location (which may include anabsolute location in GPS coordinates or other coordinate systems, and/ora relative location with respect to another vehicle or a fixed point),speed, and direction of travel. In certain examples, the nodes in a V2Vcommunication system (e.g., vehicles and other reception devices) mayuse internal clocks with synchronized time signals, and may sendtransmission times within V2V communications, so that the receiver maycalculate its distance from the transmitting node based on thedifference between the transmission time and the reception time. Thestate or usage of the vehicle's 210 controls and instruments may also betransmitted, for example, whether the vehicle is accelerating, braking,turning, and by how much, and/or which of the vehicle's instruments arecurrently activated by the driver (e.g., head lights, turn signals,hazard lights, cruise control, 4-wheel drive, traction control, etc.).Vehicle warnings such as a detection by the vehicle's 210 internalsystems that the vehicle is skidding, that an impact has occurred, orthat the vehicle's airbags have been deployed, also may be transmittedin V2V communications. In various other examples, any data collected byany vehicle sensors 211 and 221 potentially may be transmitted via V2Vcommunication to other nearby vehicles or infrastructure devicesreceiving V2V communications from communication systems 212 and 222.Further, additional vehicle driving data not from the vehicle's sensors(e.g., vehicle make/model/year information, driver insuranceinformation, driving route information, vehicle maintenance information,driver scores, etc.) may be collected from other data sources, such as adriver's or passenger's mobile device 215 or 225, driving analysisserver 250, and/or another external computer system 230, and transmittedusing V2V communications to nearby vehicles and other receiving devicesusing communication systems 212 and 222.

As shown in FIG. 2, the data collected by vehicle sensors 211 and 221also may be transmitted to a driving analysis server 250, and one ormore additional external servers and devices via telematics devices 213and 223. Telematics devices 213 and 223 may be computing devicescontaining many or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. As discussed above, thetelematics devices 213 and 223 may receive vehicle operation data anddriving data from vehicle sensors 211 and 221, and may transmit the datato one or more external computer systems (e.g., driving analysis server250 of an insurance company, financial institution, or other entity)over a wireless transmission network. Telematics devices 213 and 223also may be configured to detect or determine additional types of datarelating to real-time driving and the condition of the vehicles 210 and220. In certain embodiments, the telematics devices 213 and 223 maycontain or may be integral with one or more of the vehicle sensors 211and 221. The telematics devices 213 and 223 also may store the type oftheir respective vehicles 210 and 220, for example, the make, model,trim (or sub-model), year, and/or engine specifications, as well asother information such as vehicle owner or driver information, insuranceinformation, and financing information for the vehicles 210 and 220.

In the example shown in FIG. 2, telematics devices 213 and 223 mayreceive vehicle driving data from vehicle sensors 211 and 221, and maytransmit the data to a driving analysis server 250. However, in otherexamples, one or more of the vehicle sensors 211 and 221 may beconfigured to transmit data directly to a driving analysis server 250without using a telematics device. For instance, telematics devices 213and 223 may be configured to receive and transmit data from certainvehicle sensors 211 and 221, while other sensors may be configured todirectly transmit data to a driving analysis server 250 without usingthe telematics device 216. Thus, telematics devices 213 and 223 may beoptional in certain embodiments.

In certain embodiments, vehicle sensors, vehicle OBD, and/or vehiclecommunication systems, may collect and/or transmit data pertaining toautonomous driving of the vehicles. In autonomous driving, the vehiclefulfills all or part of the driving without being piloted by a human. Anautonomous car can be also referred to as a driverless car, self-drivingcar, or robot car. For example, in autonomous driving, a vehicle controlcomputer 217, 227 may be configured to operate all or some aspects ofthe vehicle driving, including but not limited to acceleration,deceleration, steering, and/or route navigation. A vehicle with anautonomous driving capability may sense its surroundings using thevehicle sensors 221, 221 and/or receive inputs regarding control of thevehicle from the vehicle communications systems, including but notlimited to short range communication systems 212, Telematics 213, orother vehicle communication system.

In certain embodiments, vehicle operational data can be collected fromvehicles engaged in an autonomous droning relationship. As used herein,vehicles engaged in an autonomous droning relationship include where avehicle engages in at least partial autonomous driving based onfollowing the driving of a lead or pilot vehicle. In other words, inautonomous droning, the driving of the “drone” car is automated based atleast in part on the driving of a lead vehicle. The lead vehicle can bea vehicle which the drone is following. For example, referring to FIGS.6A and 6B, the driving of vehicle 520 may be automated based on thedriving of vehicle 510. In such example, vehicle 510 can be referred toas the lead or pilot vehicle. For example, the acceleration,deceleration, braking, steering, and other operations of the dronevehicle 520 can be synchronized with that of the lead vehicle 510. Also,as shown by example in FIG. 6A, autonomous droning can be used to formcaravans or platoons of one or more vehicles 530, 520 following thedriving of the lead vehicle 510 and/or a vehicle which the drone isfollowing. The drivers of the drone vehicle can, for example, payattention to matters other than driving when the vehicle is engaged inan autonomous droning relationship. The lead vehicle can be drivenmanually, autonomously, or partially autonomously. Vehicles can engagein an autonomous droning relationship using systems of the vehicle aloneor in cooperation with systems of other vehicles. For example, a dronevehicle may rely on vehicle sensors 211 and the vehicle control computer217 to automate driving based on a lead vehicle it is following. Inaddition, a drone vehicle may use information received from vehiclecommunication systems, including V2V systems, of the lead vehicle toautomate its driving. In addition, a drone vehicle may use both vehiclesensors 211 and information from other vehicles, including a leadvehicle, to automate its driving. Vehicle operational data collectedfrom vehicles engaged in an autonomous droning relationship can includeall types of information and data described herein.

In certain embodiments, mobile computing devices 215 and 225 within thevehicles 210 and 220 may be used to collect vehicle driving data and/orto receive vehicle driving data from sensors 211 and 221, and then totransmit the vehicle driving data to the driving analysis server 250 andother external computing devices. Mobile computing devices 215 and 225may be, for example, mobile phones, personal digital assistants (PDAs),or tablet computers of the drivers or passengers of vehicles 210 and220. Software applications executing on mobile devices 215 and 225 maybe configured to detect certain driving data independently and/or maycommunicate with vehicle sensors 211 and 221 to receive additionaldriving data. For example, mobile devices 215 and 225 equipped with GPSfunctionality may determine vehicle location, speed, direction and otherbasic driving data without needing to communicate with the vehiclesensors 211 or 221, or any vehicle system. In other examples, softwareon the mobile devices 215 and 225 may be configured to receive some orall of the driving data collected by vehicle sensors 211 and 221. Mobilecomputing devices 215 and 225 may also be involved with aspects ofautonomous driving, including receiving, collecting, and transmittingvehicle operational data regarding autonomous driving and autonomousdriving relationship between multiple vehicles.

When mobile computing devices 215 and 225 within the vehicles 210 and220 are used to detect vehicle driving data and/or to receive vehicledriving data from vehicles 211 and 221, the mobile computing devices 215and 225 may store, analyze, and/or transmit the vehicle driving data toone or more other devices. For example, mobile computing devices 215 and225 may transmit vehicle driving data directly to one or more drivinganalysis servers 250, and thus may be used in conjunction with orinstead of telematics devices 213 and 223. Additionally, mobilecomputing devices 215 and 225 may be configured to perform the V2Vcommunications described above, by establishing connections andtransmitting/receiving vehicle driving data to and from other nearbyvehicles. Thus, mobile computing devices 215 and 225 may be used inconjunction with or instead of short-range communication systems 212 and222 in some examples. In addition, mobile computing devices 215 and 225may be used in conjunction with the vehicle control computers 217 and227 for purposes of autonomous driving. Moreover, the processingcomponents of the mobile computing devices 215 and 225 may be used toanalyze vehicle driving data, determine driving characteristics,determine properties related to rewards and/or aspects of vehicleinsurance, and perform other related functions. Therefore, in certainembodiments, mobile computing devices 215 and 225 may be used inconjunction with, or in place of, the driving analysis computers 214 and224.

Vehicles 210 and 220 may include driving analysis computers 214 and 224,which may be separate computing devices or may be integrated into one ormore other components within the vehicles 210 and 220, such as theshort-range communication systems 212 and 222, telematics devices 213and 223, or the internal computing systems of vehicles 210 and 220. Asdiscussed above, driving analysis computers 214 and 224 also may beimplemented by computing devices independent from the vehicles 210 and220, such as mobile computing devices 215 and 225 of the drivers orpassengers, or one or more separate computer systems 230 (e.g., a user'shome or office computer). In any of these examples, the driving analysiscomputers 214 and 224 may contain some or all of the hardware/softwarecomponents as the computing device 101 depicted in FIG. 1. Further, incertain implementations, the functionality of the driving analysiscomputers, such as storing and analyzing vehicle driving data,determining driving characteristics, and determining rewards and/oraspects of insurance polies, may be performed in a central drivinganalysis server 250 rather than by individual vehicles 210 and 220. Insuch implementations, the vehicles 210 and 220 might only collect andtransmit vehicle driving data to a driving analysis server 250, and thusthe vehicle-based driving analysis computers 214 and 224 may beoptional.

Driving analysis computers 214 and 224 may be implemented in hardwareand/or software configured to receive vehicle driving data from vehiclesensors 211 and 221, short-range communication systems 212 and 222,telematics devices 213 and 223, vehicle control computer 217 and 227and/or other driving data sources. Vehicle sensors/OBDs 211 and 221,short-range communication systems 212 and 222, telematics devices 213and 223, vehicle control computer 217 and 227 and/or other driving datasources can be referred to herein individually or collectively as avehicle data acquiring component. The driving analysis computer 214, 224may comprise an electronic receiver to interface with the vehicle dataacquiring components to receive the collected data. After receiving, viathe electronic receiver, the vehicle driving data from, for example avehicle data acquiring component, the driving analysis computers 214 and224 may perform a set of functions to analyze the driving data,determine driving characteristics, and determine properties related todriver rewards and/or vehicle insurance. For example, the drivinganalysis computers 214 and 224 may include one or more drivingcharacteristic algorithms, which may be executed by software running ongeneric or specialized hardware within the driving analysis computers.The driving analysis computer 214 in a first vehicle 210 may use thevehicle driving data received from that vehicle's sensors 211, alongwith vehicle driving data for other nearby vehicles received via theshort-range communication system 212, to determine drivingcharacteristics and determine properties related to rewards and/orvehicle insurance applicable to the first vehicle 210 and the othernearby vehicles. Within the driving analysis computer 214, a vehicleinsurance property reward function may use the results of the drivinganalysis performed by the computer 214 to determine/adjust a property ofan insurance policy for a driver of a vehicle 210 or other vehiclesbased on determined driving characteristics. Further descriptions andexamples of the algorithms, functions, and analyses that may be executedby the driving analysis computers 214 and 224 are described below inreference to FIGS. 3 and 4.

The system 200 also may include a driving analysis server 250,containing some or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. The driving analysis server 250may include hardware, software, and network components to receivevehicle operational data/driving data from one or more vehicles 210 and220, and other data sources. The driving analysis server 250 may includea driving data and driver data database 252 and driving analysiscomputer 251 to respectively store and analyze driving data receivedfrom vehicles and other data sources. The driving analysis server 250may initiate communication with and/or retrieve driving data fromvehicles 210 and 220 wirelessly via telematics devices 213 and 223,mobile devices 215 and 225, or by way of separate computing systems(e.g., computer 230) over one or more computer networks (e.g., theInternet). Additionally, the driving analysis server 250 may receiveadditional data relevant to driving characteristic or otherdeterminations from other non-vehicle data sources, such as externaltraffic databases containing traffic data (e.g., amounts of traffic,average driving speed, traffic speed distribution, and numbers and typesof accidents, etc.) at various times and locations, external weatherdatabases containing weather data (e.g., rain, snow, sleet, and hailamounts, temperatures, wind, road conditions, visibility, etc.) atvarious times and locations, and other external data sources containingdriving hazard data (e.g., road hazards, traffic accidents, downedtrees, power outages, road construction zones, school zones, and naturaldisasters, etc.), and insurance company databases containing insurancedata (e.g., driver score, coverage amount, deductible amount, premiumamount, insured status) for the vehicle, driver, and/or other nearbyvehicles and drivers.

Data stored in the driving data database 252 may be organized in any ofseveral different manners. For example, a table in database 252 maycontain all of the vehicle operation data for a specific vehicle 210,similar to a vehicle event log. Other tables in the database 252 maystore certain types of data for multiple vehicles. For instance, tablesmay store specific data sets, including correlations related to thevehicle for use in determining driving characteristics and/or propertiesrelated to driver rewards and/or vehicle insurance.

The driving analysis computer 251 within the driving analysis server 250may be configured to retrieve data from the driving data and driverscore database 252, or may receive driving data directly from vehicles210 and 220 or other data sources, and may perform driving dataanalyses, driving characteristic determinations, driver reward and/orvehicle insurance determinations, and other related functions. Thefunctions performed by the driving analysis computer 251 may be similarto those of driving analysis computers 214 and 224, and furtherdescriptions and examples of the algorithms, functions, and analysesthat may be executed by the driving analysis computer 251 are describedbelow in reference to FIGS. 3 and 4.

In various examples, the driving data analyses, driving characteristicdeterminations, and driving reward and/or insurance propertydeterminations may be performed entirely in the driving analysiscomputer 251 of the driving analysis server 250 (in which case drivinganalysis computers 214 and 224 need not be implemented in vehicles 210and 220), or may be performed entirely in the vehicle-based drivinganalysis computers 214 and 224 (in which case the driving analysiscomputer 251 and/or the driving analysis server 250 need not beimplemented). In other examples, certain driving data analyses may beperformed by vehicle-based driving analysis computers 214 and 224, whileother driving data analyses are performed by the driving analysiscomputer 251 at the driving analysis server 250. For example, avehicle-based driving analysis computer 214 may continuously receive andanalyze driving data from nearby vehicles to determine certain drivingcharacteristics (e.g., aspects related to vehicle drafting, aspectsrelated to autonomous driving, etc.) so that large amounts of drivingdata need not be transmitted to the driving analysis server 250.However, for example, after driving characteristic is determined by thevehicle-based driving analysis computer 214, the characteristic may betransmitted to the server 250, and the driving analysis computer 251 maydetermine if a driver reward and insurance vehicle property should beupdated based on the determined driving characteristic.

FIG. 3 is a flow diagram illustrating an example method of determining aproperty of an insurance policy based on analysis of vehicle operationaldata of vehicles engaged in an autonomous droning relationship. Thisexample method may be performed by one or more computing devices in adriving analysis system, such as vehicle-based driving analysiscomputers 214 and 224, a driving analysis computer 251 of a drivinganalysis server 250, user mobile computing devices 215 and 225, and/orother computer systems.

The steps shown in FIG. 3 describe performing an analysis of vehicleoperational data to determine driving characteristics of vehiclesengaged in an autonomous droning relationship and determining a propertyof an insurance policy based on the determined property. In step 301,vehicle operational data may be received from a first vehicle 210. Asdescribed above, a driving analysis computer 214 may receive and storevehicle driving data from a vehicle data acquiring component, includingbut not limited to the vehicle's internal computer systems and anycombination of the vehicle's sensors 211 and/or communication systems.The data received in step 301 may include, for example, an identifierthat the vehicle is engaged in autonomous driving and/or an autonomousdriving relationship with another vehicle. The data received in step 301may include, for example, the location, speed, and direction of thevehicle 210, object proximity data from the vehicle's external camerasand proximity sensors, and data from the vehicle's various systems usedto determine if the vehicle 210 is braking, accelerated, or turning,etc., and to determine the status of the vehicle's user-operatedcontrols (e.g., head lights, turn signals, hazard lights, radio, phone,etc.), along with any other data collected by vehicle sensors 211 ordata received from a nearby vehicle.

In step 302, the vehicle operational data is analyzed to determinewhether the vehicle is engaged in an autonomous droning relationshipwith another vehicle. For example, the driving data may include anidentifier which indicates that the vehicle is engaged in an autonomousdroning relationship. In addition, for example, a driving analysiscomputer 214 in a first vehicle 210 may compare the driving data (e.g.,location, speed, direction) from its own vehicle sensors 211 (receivedin step 301) with the corresponding driving data (e.g., location, speed,direction) from a nearby vehicle 220. Based on the relative locations,speeds, and directions of travel of vehicles 210 and 220, the drivinganalysis computer 214 may determine that the vehicles are engaged in anautonomous driving relationship. In an embodiment, the driving data ofthe nearby vehicle can be collected by the data acquiring component ofthe first vehicle 210 via, for example, vehicle V2V. In an embodiment,the driving data of the nearby vehicle can be received from the nearbyvehicle directly. In an embodiment, the driving data of the nearbyvehicle can be determined from vehicle sensors 211 of the first vehicle.

In step 303, the vehicle driving data received in steps 301 may beanalyzed, and driving characteristics of the autonomous droningrelationship may be determined for the vehicles 210 and 220 based on thedriving data. A driving characteristic of the autonomous droningrelationship may include any number actions or events performed by thevehicles in the relationship or driving conditions or circumstancesimpacting the autonomous droning relationship. For example, acharacteristic of an autonomous droning relationship can includeidentification of a lead vehicle, identification of the drone vehicle,an amount of time or distance a vehicle is the lead vehicle, an amountof time or distance a vehicle is a drone vehicle, that the lead vehicleis engaged in manual or autonomous driving, the number of vehiclesengaged in the autonomous driving relationship, a weather condition,and/or a driver safety rating. The driving characteristic of theautonomous droning relationship can be determined by, for example,identifying the pertinent information from the received data, includingactions or events performed by the vehicle or nearby vehicles. Forexample, the driving data may include a data point that identifies avehicle as a lead vehicle in an autonomous driving relationship. Inaddition, for example the driving data may include time marked data fromwhen a vehicle begins droning to when it completes droning. The drivingcharacteristic can also be calculated using selected driving data. Forexample, if the driving data includes an identifier for when the vehicleis engaged in autonomous driving as a drone and time marked vehiclespeed, the distance in which the vehicle is engaged in the autonomousdriving relationship as a drone can be calculated. Example algorithmsusing time marked driving data are included in US Publications Number2013/0073112 which is hereby incorporated by reference herein in itsentirety. The driving characteristic can also be identified by comparingthe driving data associated with a first vehicle with the correspondingdriving data of a nearby vehicle. Based on information from, forexample, the relative locations, speeds, and directions of travel ofvehicles 210 and 220, the driving analysis computer 214 may determine adriving characteristic of the autonomous droning relationship involvingthe two vehicles.

In step 304, a property of an insurance policy may be determined usingthe characteristic of the autonomous droning relationship. The propertyof an insurance policy can include any of a number of aspects of avehicle insurance policy. For example, a property of an insurance policycan include a premium, a deductible, a coverage term, a coverage amount,or other attribute of an insurance policy. In various embodiments, theproperty can be determined in accordance with rules set forth by theinsurance provider. For example, the property of the vehicle insurancepolicy may change depending upon the characteristic of the autonomousdroning relationship. For example, if the characteristic is that a firstvehicle is a lead vehicle in an autonomous droning relationship, thedeductible of an insurance policy of the first vehicle may increase inaccordance with the number of vehicles engaged as drones. In addition,for example, a premium may decrease if the characteristic is that afirst vehicle is engaged as a drone vehicle with a nearby vehicle whichis piloted by a driver with a strong safety record and/or good driversafety rating.

In addition, in various embodiments, step 304 can include an additionalstep of determining an autonomous droning insurance factor using atleast one of the vehicle operational data and the characteristic of theautonomous droning relationship and determine a property of an insurancepolicy using the autonomous droning insurance factor. As used herein,the autonomous droning insurance factor can include a variable used inan algorithm for calculating a property of an insurance policy. Forexample, an autonomous droning insurance factor can include a ratio ofan amount of time a first vehicle is engaged in an autonomous droningrelationship versus a total amount of time the first vehicle has drivenover a period of time and/or a ratio of an amount of distance the firstvehicle is engaged in an autonomous droning relationship versus a totaldistance the first vehicle has driven over a period of time. The factorcan be used in an algorithm for calculating an insurance premium by, forexample, assessing different rates for the periods of time or distancewhich the vehicle is engaged in autonomous driving.

In addition, in various embodiments, information pertaining to aninsurance policy of a second vehicle can be used in determining theproperty of the first insurance policy of the first vehicle. Forexample, if the driving data includes a coverage amount of a vehicleengaged in the autonomous droning relationship and the coverage amountdoes not exceed a threshold level, the deductible of the first vehiclecan increase.

In step 305, the driving analysis computer can adjust or cause to adjustthe insurance policy based on the determined property. In variousembodiments, the adjustment can occur during the coverage term and/orprior to the subsequent coverage term. In addition, the policy holdermay be notified of the adjustment. Alternatively, the adjustment cancome in the form of a reward to be received by the party to which it isallocated. For example, the system may cause the remunerative payment tobe debited from a bank account associated the payor (e.g., a vehicleinsurance company) and credited to the account of the policy holder ofthe insurance policy. As such, the paying party (e.g., the policyholder) may enjoy the benefit of the remunerative payment.

As shown in FIG. 3, a single vehicle-based driving analysis computer 214may receive driving data for a first vehicle 210 (step 301), includingdriving data received from V2V communications including driving data forone or more other vehicles, may determine from the data whether thevehicle is engaged in an autonomous driving relationship (step 302), andmay determine a characteristic of an autonomous droning relationship(step 303), determine a property of an insurance policy based on thecharacteristic (step 304), and adjust the insurance policy based on thedetermined property (step 305). However, other driving analysiscomputers and/or other computing devices may be used to some or all ofthe steps and functionality described above in reference to FIG. 3. Forexample, any of steps 301-305 may be performed by a user's mobile device215 or 225 within the vehicles 210 or 220. These mobile devices 215 or225, or another computing device 230, may execute software configured toperform similar functionality in place of the driving analysis computers214 and 224. Additionally, some or all of the driving analysisfunctionality described in reference to FIG. 3 may be performed by adriving analysis computer 251 at a non-vehicle based driving analysisserver 250. For example, vehicles 210 and 220 may be configured totransmit their own vehicle sensor data, and/or the V2V communicationsdata received from other nearby vehicles, to a central driving analysisserver 250 via telematics devices 213 and 223. In this example, thedriving analysis computer 251 of the server 250 may perform the dataanalysis of steps 302 through 305.

FIG. 5 is a flow diagram of an illustrative method of awarding (e.g.,crediting) one or more user accounts with a reward amount based oninvolvement in an autonomous droning relationship. The illustrativemethod steps may be performed by one or more computing devices in anautonomous droning reward system, including, but not limited to, avehicle-based driving analysis computers 214 and 224, a driving analysiscomputer 251 of a driving analysis server 250, user mobile computingdevices 215 and 225, and/or other computer systems.

Regarding step 502, an autonomous droning reward system may comprise anautonomous droning relationship determinator 226B configured to receivedriving data indicating a lead vehicle 220 in an autonomous droningrelationship with at least one following vehicle 210. The autonomousdroning relationship determinator 226B may determine/identify when avehicle is in an autonomous droning relationship. In some examples, avehicle's data acquiring component may collect vehicle operation dataand use some of that data to generate driving data. In other examples,vehicle operation data may be used interchangeably with driving data. Inany event, the autonomous droning relationship determinator 226B may usethe driving data to determine when one or more vehicles are in anautonomous droning relationship.

As explained above in accordance with various aspects of the disclosure,driving characteristic of an autonomous droning relationship can bedetermined by, for example, identifying pertinent information in thereceived data, including actions or events performed by vehicles 210,220 or driving conditions or circumstances impacting the autonomousdroning relationship. For example, the driving data may include anexpress data point/message/packet that identifies a vehicle (e.g., byits VIN number or other identifier) as a lead vehicle 220 in anautonomous driving relationship. In addition, the driving data mayinclude a time-stamped message indicating that a following vehicle hasstarted droning until when it completed droning. From such data, thedistance traveled when in an autonomous droning relationship may becalculated. Similarly, the amount of time in the autonomous droningrelationship may be calculated.

Furthermore, in some examples, vehicle driving data may be analyzed, andthe driving characteristic of the autonomous droning relationship may bedetermined for the involved vehicles 210 and 220. For example, acharacteristic of an autonomous droning relationship may includeidentification of a lead vehicle, identification of the drone vehicle,an amount of time or distance a vehicle is the lead vehicle, an amountof time or distance a vehicle is a drone vehicle, that the lead vehicleis engaged in manual or autonomous driving, the number of vehiclesengaged in the autonomous droning relationship, a weather condition, adriver safety rating, and/or other characteristics. The drivingcharacteristic can also be identified by comparing the driving data of afollowing vehicle 210 with driving data of a nearby vehicle 220. Basedon information from, for example, the relative locations, speeds, anddirections of travel of the vehicles 210, 220, autonomous droningrelationship determinators 226A, 226B may determine that the twovehicles are in an autonomous droning relationship. Moreover, theautonomous droning relationship determinators 226A, 226B, incoordination with other computing devices 214, 224 at the vehicle, maydetermine the total number of vehicles engaged in the autonomous droningrelationship. For example, three vehicles may be involved in anautonomous droning relationship, as illustrated in FIG. 6A.

While systems already exist for autonomous vehicles, such as theself-driving car by GOOGLE™, the spirit of this disclosure is notlimited to just autonomous self-driving cars. For example, the leadvehicle 220 may be a completely autonomous vehicle, semi-autonomousvehicle, or a manual human-driven vehicle. Depending on the capabilitiesof the lead vehicle 220, the vehicle may be equipped with theappropriate sensors 221 and other electronic components to enable theautomation/semi-automation, as is already known in the relevant art ofautonomous/semi-autonomous vehicles. Similarly, a following (drone)vehicle 210 may be equipped with the appropriate hardware and softwareto operate as an autonomous vehicle, semi-autonomous vehicle, or amanually-driven vehicle. In contrast, however, in some examples, thefollowing drone vehicle 210 may be equipped with less hardware and/orsoftware than a lead vehicle 220 because to some extent, the followingdrone vehicle 210 may rely upon the lead vehicle 220 to provide guidanceand commands for controlling the speed, acceleration, braking,cornering, route, and other operation of the following vehicle 210. Forexample, a following drone vehicle 210 may transmit data to the leadvehicle 220 using its short-range wireless communications system 212,and rely upon long-range wireless communication capabilities 222 of thelead vehicle to forward the data to the appropriate final destination.At least one benefit of such an arrangement is that the cost/price of afollowing drone vehicle 210 may be less than that of other vehicles(e.g., lead vehicle 220) due to reduced complexity and reduce hardwareand/or software requirements.

In addition, the integrity of collected vehicle driving data may bevalidated by comparing, e.g., by a driving analysis computer, thedriving data (e.g., location, speed, direction) from one vehicle'ssensors 211 with corresponding driving data from a nearby vehicle 220.Based on the relative locations, speeds, and directions of travel ofvehicles 210 and 220, an autonomous droning relationship determinator226B of a driving analysis computer 214 may determine that the vehiclesare engaged in an autonomous driving relationship. In one example,driving data of the nearby vehicle can be collected by a data acquiringcomponent of a following/drone vehicle 210 via, for example, vehicleV2V. In one example, the driving data of the nearby vehicle may bedirectly received from the nearby vehicle.

In accordance with various aspects of the disclosure, in one example, anautonomous droning relationship determinator 226 may receive (in step502) driving data indicating that a lead vehicle 220 is in an autonomousdroning relationship with at least one following vehicle 210. Theautonomous droning relationship determinator 226A may be executing in adriving analysis computer 224 at the lead vehicle 220. Alternatively,the autonomous droning relationship determinator 226B may be executingin a driving analysis computer 214 at one of the following/dronevehicles 210. In yet another alternative, the autonomous droningrelationship determinator 226 may be distributed across the lead vehicle220 and one or more following vehicles 210 such that the determinationof whether a vehicle is involved in an autonomous droning relationshipinvolves coordination/communication between more than one autonomousdroning relationship determinators 226A, 226B.

Continuing with the preceding example, the autonomous droningrelationship determinator 226 may determine, in this particular example,that an autonomous droning relationship persists so long as the leadvehicle 220 transmits commands (e.g., vehicle control commands) to theat least one following vehicle 210 on at least a regular interval (e.g.,every second, every half second, every couple seconds, or other intervalof time). The commands, upon receipt at an interface to a vehiclecontrol computer 217 at the following vehicle 210, may cause thefollowing drone vehicle 210 to control/alter its driving characteristicspursuant the commands. Examples of changes in driving characteristicsinclude, but are not limited to, changing the speed of the drone vehicle210, changing the direction of travel of the drone vehicle 210, and/orturning on/off headlights of the drone vehicle 210. Moreover, somecommands may be to cause the drone vehicle 210 to hold steady its speed;although such commands might not expressly alter the drivingcharacteristics of the vehicle 210, the command does cause the vehiclecontrol computer 217 to control the vehicle 210. A person havingordinary skill in the art, after review of the entirety disclosedherein, will appreciate that the disclosure contemplates other examplesof commands that alter the driving characteristics of a drone vehicle.

The lead vehicle 220 may generate commands and transmit them to one ormore following vehicles 210. In one example, the driving analysiscomputer 224 of the lead vehicle 220 may take driving data as input andgenerate commands based on that driving data. For example, if the OBDinterface 221 of the lead vehicle 220 indicates that the lead vehicle220 is traveling at a speed of 45 mph in a particular cardinal direction(e.g., North), that information may be used to generate commands that afollowing drone vehicle 210 may process to adjust its speed and/ordirection. A person having ordinary skill in the art, after review ofthe entirety disclosed herein, will appreciate that systems forautonomous droning are known and that aspects of these systems areincorporated into the illustrative system shown in FIG. 2.

In the preceding example, the autonomous droning relationshipdeterminator 226B of a following vehicle 210 monitors for incomingcommands on a regular interval, and if valid incoming commands aredetected, the autonomous droning relationship determinator 226Bdetermines that the following vehicle 210 is in an autonomous droningrelationship. Likewise, a lead vehicle 220, using its short-rangecommunications system (see FIG. 2), may regularly send/broadcastcommands to one or more following drone vehicles; and while it iscontinuing to send such commands, may notify the autonomous droningrelationship determinator 226A that the lead vehicle 220 is in anautonomous droning relationship.

In another example, the autonomous droning relationship determinator 226may determine that the vehicle 210, 220 has entered into an autonomousdroning relationship when an electronic handshake between the vehiclesis sent, acknowledged, and/or established. With the handshake protocolcompleted, an autonomous droning relationship is established. Meanwhile,the autonomous droning relationship determinator 226 may monitorcommunications between its vehicle and the nearby vehicle todetect/determine when the autonomous droning relationship hasterminated. The driving data (and optionally other data) collected whilein the autonomous droning relationship may be sent to an autonomousdroning reward calculator 702 for calculation of a possible rewardamount.

In step 504, at or after termination of the autonomous droningrelationship, an autonomous droning reward calculator 702 may calculatea reward amount. Alternatively, the autonomous droning reward calculator702 may maintain a running count of the reward amount during theautonomous droning relationship. The running count may allow theautonomous droning reward calculator 702 to display in real-time, e.g.,on a display screen located in the vehicle, the pending reward amount.

Referring to FIG. 7, an autonomous droning reward calculator 702 maycalculate the reward amount using at least driving data. The drivingdata may include, but is not limited to, at least one of: speed of thelead vehicle, location of the lead vehicle, amount of time spent in theautonomous droning relationship, amount of distance traveled in theautonomous droning relationship, and/or other characteristics. Otherexamples of driving data include the time of day when the autonomousdroning relationship exists, the following distance between the leadvehicle and a following drone vehicle (or in the case of multiplefollowing vehicles, such as illustrated in FIG. 6, the distance betweena vehicle and the vehicle immediately behind it), the vehicle'scharacteristics (e.g., vehicle type—SUV, sports car, sedan, convertible,etc., vehicle's turning radius, vehicle's maximum speed, vehicle time toaccelerate from 0-60 mph, and other characteristics tied to the specificvehicle), driving risk characteristics/profile of the driver/operator ofthe lead vehicle 220, and other characteristics. For example, assumingnumerous systems exist for automated droning, “System A” may usehardware and/or software algorithms different from that of competing“System B.” As a result, each of the systems may react differently whenused in the real world, and as such, will earn a driving riskcharacteristic/profile commensurate with the amount of risk associatedwith the particular system. Therefore, an autonomous droning rewardcalculator 702 may calculate a higher reward amount for “System A” thanfor “System B,” in some examples. In another example where the leadvehicle 220 is manually driven, either the entire time of a trip or justa portion of the trip, by a human, the driving risk characteristics maybe tied to the driver's driving risk score, which may take into accountfactors such as number of accidents, speeding ticket violations, numberof submitted insurance claims, and other factors known for use incalculating a driving risk score.

In addition, referring to FIG. 7, other information may also be inputtedinto the autonomous droning reward calculator 702 for consideration incalculating a reward amount. For example, the congestion level (e.g.,traffic) on a roadway where the lead vehicle 220 is being followed bydrone vehicles may be an input to determining a reward amount. Otherexamples include, but are not limited to, the weather conditions theroadway, historical occurrences of incidents (e.g., vehicular accidents)on the roadway, and other factors related to theenvironment/surroundings in which the vehicle is operated. For example,the autonomous droning reward calculator 702 may adjust the rewardamount based on the congestion level on the roadway being high. In oneexample, the autonomous droning reward calculator 702 may increasereward amounts during rush hour traffic to encourage vehicles 210equipped with a vehicle control computer 217 to drone/follow a leadvehicle, thus alleviate traffic congestion. Congestion levels may bedivided, in one example, into categories of high, medium, and low basedon the whether the travel time through a particular roadway falls intothe upper ⅓, middle ⅓, or lower ⅓ of possible travels times historicallylogged on that roadway. Likewise, weather conditions may play a role indetermining the reward amount. In a fog situation, reward amounts may bevery high in order to encourage both drone/following vehicles 210 andappropriate lead vehicles 220 (e.g., those vehicles that have been ratedsafe for operation in fog conditions, for example, because they areequipped with fog lights or other fog-ready equipment) to enter into anautonomous droning relationship.

The aforementioned external information from the preceding example ofFIG. 7 may be stored at and retrieved from various data sources, such asan external traffic databases containing traffic data (e.g., amounts oftraffic, average driving speed, traffic speed distribution, and numbersand types of accidents, etc.) about various times and locations,external weather databases containing weather data (e.g., rain, snow,sleet, and hail amounts, temperatures, wind, road conditions,visibility, etc.) at various times and locations, and other externaldata sources containing driving hazard data (e.g., road hazards, trafficaccidents, downed trees, power outages, road construction zones, schoolzones, and natural disasters, etc.), and insurance company databasescontaining insurance data (e.g., driver score, coverage amount,deductible amount, premium amount, insured status) for the vehicle,driver, and/or other nearby vehicles and drivers. The aforementionedexternal information may, in some examples, be wirelessly transmittedfrom a remote server and/or database to the vehicle 220 forconsideration by the autonomous droning reward calculator 702. Asexplained earlier, drone vehicles 210 may leverage additional hardwareand/or software capabilities of a lead vehicle 220 to gain access to theaforementioned information, when desired. For example, a lead vehicle220 may receive, through its long-range communications circuitry 222 (ormobile phone 225), the information and forward it to drone vehicles 210via their short-range communications 212 systems. As such, the vehicles210, 220 may input the information into their autonomous droning rewardcalculator 702 for consideration.

FIG. 7 shows the autonomous droning reward calculator 702 receivingnumerous inputs and outputting an autonomous droning reward amount. Insome examples, the autonomous droning reward calculator 702 may be anapplication-specific integrated circuit (ASIC) designed to perform thefunctionality described herein. In other examples, autonomous droningreward calculator 702 may use a processing unit (e.g., comprising acomputer processor, such as an Intel™ x86 microprocessor or otherspecial-purpose processors) and computer-executable instructions storedin a memory to cause a driving analysis computer 214 to perform thesteps described herein.

Referring to step 504, the autonomous droning reward calculator 702 maycalculate a reward amount using one or more of the numerous driving dataand other information described herein. In one example, the autonomousdroning reward calculator 702 may increase the reward amount as theamount of distance traveled while in the autonomous droning relationshipincreases. In another example, the autonomous droning reward calculator702 may increase the reward amount if the speed of the lead vehiclestays within a threshold speed range while in the autonomous droningrelationship. In other words, if the vehicle speed of the caravan iskept within a safe range, a higher reward amount may be awarded. In yetanother example, the autonomous droning reward calculator 702 mayincrease the reward amount if the location of the lead vehicle stayswithin a geographic region while in the autonomous droning relationship.In another example, the autonomous droning reward calculator 702 mayincrease the reward amount as the amount of time spent while in theautonomous droning relationship increases.

FIG. 8 show a graphic illustration of the preceding example in which theautonomous droning reward calculator 702 increases the reward amount asthe amount of time spent in while in the autonomous droning relationshipincreases. Graph 800A shows that in some examples, the autonomousdroning reward calculator 702 may be configured to provide a rewardamount in a linear proportion to the amount of time. As such, the graphis a straight line. Meanwhile, graph 800B shows an example where theautonomous droning reward calculator 702 may be configured to provide areward amount as a function of the amount of time, but with a predefinedmaximum reward amount. As such, the relationship is defined such thatonce a drone vehicle 210 has been in an autonomous droning relationshipfor an extended period of time, the portion of the reward amount, whichmay be due to a measure of the amount of time in an autonomous droningrelationship, remains generally the same value. Along these same lines,the autonomous droning reward calculator 702 may be configured withvarying formulas/functions that establish relationships (e.g., linear,exponential, logarithmic, etc.) between the various inputs (e.g., suchas those illustrated in FIG. 7) and the reward amount.

While in some examples the reward amount is a dollar amount (e.g., $10,$5.75, $0.98, etc.), in other examples, the reward amount may be anon-dollar amount. For example, the autonomous droning reward amount maybe a percentage value (e.g., a 1.2% discount, a 0.5% discount, and a2.0% upcharge) applied with respect to a vehicle insurance policy,either prospectively and/or retrospectively to the beginning of thecurrent policy term. In yet another example, the reward amount may be avoucher or coupon for goods and/or services.

In step 506, with the autonomous droning reward amount having beencalculated in step 504, the reward amount is credited to the appropriateparty, such as an account associated with the lead vehicle or a dronevehicle. In some examples, the lead vehicle 220 may receive the entirereward amount. In other examples, the reward amount may be divided andcredited to all of the vehicle involved in the autonomous droningrelationship. For example, a reward amount of $10 may be calculated instep 504, and $7 of it may be credited (in step 506) to a bank checkingaccount associated with the lead vehicle 220, $2 of it may be credited(in step 506) to a bank checking account associated with the dronevehicle 210, and the remaining $1 may be credited to one or moregovernment or private entities involved in the autonomous droning rewardsystem. Examples of such government and/or private entities mightinclude an insurance company providing the vehicle insurance policy tothe lead vehicle, an insurance company providing vehicle insurancepolicy to the drone vehicle 210, a State's department of transportation,or other entity.

With reference to FIG. 9, an account processor 902 executing an accountmodule 254 may serve to credit and/or debit, as appropriate, a rewardsamount amongst various entities. In one example, a vehicle insurancecompany 904 insuring the lead vehicle 220 may provide the full rewardamount. In other examples, the vehicle insurance company(s) 906corresponding to one or more drone vehicles 210 following the leadvehicle may be responsible for crediting the autonomous droning rewardamount to the lead vehicle's account 256A. In some hybrid-approaches,multiple insurance companies, including that of the lead vehicle anddrone vehicle(s), may share the responsibility for awarding theautonomous droning reward amount. In other examples, the autonomousdroning reward amount awarded to the lead vehicle's account may beprovided by a drone vehicle (e.g., the account 256B associated with thedrone vehicle may be debited accordingly).

Numerous different types of accounts are contemplated in accordance withvarious aspects of the disclosure. In some examples, a user's checkingaccount at a financial institution may be credited and debited based onthe autonomous droning reward amount calculated using at least drivingdata. The financial institution may be in communication, either directlyor indirectly, with the autonomous droning reward system. As a result,usage-based rewards may be implemented in an autonomous droning rewardsystem. In other examples, a vehicle's insurance policy account at avehicle insurance company may be credited and debited based on theautonomous droning reward amount calculated using at least driving data.The insurance company may be in communication, either directly orindirectly, with the autonomous droning reward system.

Aspects of the disclosure pertain to analyzing driving data to determinedriving characteristics associated with vehicle drafting relationships,determine driver rewards based on such characteristics, and allocatingthe driving rewards. Vehicle drafting, also referred to asslipstreaming, as used herein pertains to when two or more vehicles movein alignment to reduce the overall effect of drag due to exploiting thelead vehicle's slipstream. As a vehicle moves, high pressure isgenerated in front and low pressure behind. The difference in pressurecreates a force known as drag. Drag force can account for a large amountof fuel consumption, especially at high speeds. The concept of draftingor slipstreaming is to utilize the regions of reduced pressure behindmoving vehicles to lessen the oncoming drag experienced by the followingvehicle, thereby reducing fuel consumption of the following vehicle.Slipstreaming can also reduce the fuel consumption of the lead vehiclebut to a lesser extent than the lead vehicle. However, in order torealize appreciable savings in fuel consumption by engaging in drafting,vehicles may have to follow at distances less than legal followingdistances for manual driving. For example, a University ofCalifornia-San Diego study determined that drag reduction was negligibleat a distance of 288 feet at 65 mph and, therefore, show no increase infuel savings. See Duan et al., Effects of Highway Slipstreaming onCalifornia Gas Consumption, MAE 171B-Mechanical Lab II, (Jun. 13, 2007)(hereinafter referred to as the “Duan Report”), which is herebyincorporated by reference herein in its entirety and a copy of which isfiled in the Information Disclosure Statement concurrently filed withthis application. However, autonomous driving and driving in autonomousdroning relationships can allow for following at close distances in asafe manner. Accordingly, fuel savings associated with drafting can berealized utilizing autonomous driving and autonomous droning.

FIG. 4 is a flow diagram illustrating an example method of determining adrafting property associated with a first vehicle engaged in a draftingrelationship and allocating a drafting reward based on the draftingproperty. This example method may be performed by one or more computingdevices in a driving analysis system, such as vehicle-based drivinganalysis computers 214 and 224, a driving analysis computer 251 of adriving analysis server 250, user mobile computing devices 215 and 225,and/or other computer systems.

The steps shown in FIG. 4 describe performing analysis of vehicleoperational data to determine drafting characteristics of vehiclesengaged in a drafting relationship, determine a drafting property basedon the drafting characteristic, and allocate a drafting award based onthe drafting property. In step 401, vehicle operational data may bereceived from a first vehicle 210. As described above, a drivinganalysis computer 214 may receive and store vehicle driving data from avehicle data acquiring component, including but not limited to thevehicle's internal computer systems and any combination of the vehicle'ssensors 211 and/or communication systems. The data received in step 401may include, for example, an identifier that the vehicle is engaged in adrafting relationship. The data received in step 401 may also include,for example, the location, speed, and direction of the vehicle 210,object proximity data from the vehicle's external cameras and proximitysensors, and data from the vehicle's various systems used to determineif the vehicle 210 is braking, accelerating, or turning, etc., and todetermine the status of the vehicle's user-operated controls (e.g., headlights, turn signals, hazard lights, radio, phone, etc.), vehicle type,along with any other data collected by vehicle sensors 211 or datareceived from a nearby vehicle.

In step 402, the vehicle operational data is analyzed to determinewhether the vehicle is engaged in a drafting relationship with anothervehicle. For example, the driving data may include an identifier whichindicates that the vehicle is engaged in a drafting relationship. Inaddition, for example, a driving analysis computer 214 in a firstvehicle 210 may compare the driving data (e.g., location, speed, vehicletype) from its own vehicle sensors 211 (received in step 401) with thecorresponding driving data (e.g., location, speed, vehicle) from anearby vehicle 220. Based on the relative locations, speeds, and vehicletypes of vehicles 210 and 220, the driving analysis computer 214 maydetermine that the vehicles are engaged in a drafting relationship. Inan embodiment, the driving data of the nearby vehicle can be collectedby the data acquiring component of the first vehicle 210 via, forexample, vehicle V2V. In an embodiment, the driving data of the nearbyvehicle can be received from the nearby vehicle directly. In anembodiment, the driving data of the nearby vehicle can be determinedfrom vehicle sensors 211 of the first vehicle.

In step 403, the vehicle driving data received in steps 401 may beanalyzed, and driving characteristics of the drafting relationship maybe determined for the vehicles 210 and 220 based on the driving data. Adriving characteristic of the drafting relationship may include anynumber actions or events performed by the vehicles in the relationshipor driving conditions or circumstances impacting the draftingrelationship. For example, a characteristic of a drafting relationshipcan include identification of a lead vehicle, identification of thefollowing vehicle, vehicle spacing, vehicle speeds, vehicle types,miles-per-gallon readings, a weather condition, whether the vehicles areengaged in autonomous driving, and/or a driver safety rating. Thedriving characteristic of the drafting relationship can be determinedby, for example, identifying the pertinent information from the receiveddata, including actions or events performed by the vehicle or nearbyvehicles. For example, the driving data may include a data point thatidentifies a vehicle as a lead vehicle in a drafting relationship. Thedriving characteristic can be determined by calculations using selecteddriver data. For example, vehicle spacing can be calculated from vehiclelocation data by, for example, identifying the locations of therespective vehicles and performing an analysis to determine the distancebetween the vehicles. The driving characteristic can also be identifiedby comparing the driving data associated with a first vehicle with thecorresponding driving data of the nearby vehicle. Based on informationfrom, for example, the relative locations, speeds, and vehicle types ofvehicles 210 and 220, the driving analysis computer 214 may determine adriving characteristic of the drafting relationship involving the twovehicles. As used herein, a driving characteristic of a draftingrelationship may also be referred to as a drafting characteristic.

In step 404, a drafting property is determined based on the draftingcharacteristic. As used herein, a drafting property can include anymeasure of efficiency realized from the drafting relationship. Forexample, a drafting property can include a drafting fuel savings rate, apercentage increase in miles-per-gallon, and a drafting fuel savingsamount. In various embodiments, the drafting property can be determinedfrom algorithms or identified from predetermined correlations based onthe drafting characteristics. For example, where the drafting propertyis a drafting fuel savings amount, the drafting fuel savings amount canbe determined by subtracting from a miles-per-gallon reading taken whilethe vehicle is drafting a miles-per-gallon reading taken while thevehicle is not drafting to determine a drafting miles-per-gallon savingsrate and dividing a distance traveled by the vehicle by the firstdrafting miles-per-gallon savings rate. In addition, for example, thedrafting property can be determined from a correlation of vehiclespacing vs. an increase in miles-per-gallon for specific vehicle. Suchcorrelations can be based on, for example, vehicle lead type, vehiclefollowing type, vehicle spacing, and vehicle speed. Such correlationscan be prepared by, for example, methods including modeling and or windtunnel experiments. Example methods for preparing such correlationsinclude those disclosed in the Duan Report. Correlations for use indetermining drafting properties can be stored on a driving data database252.

In step 405, the driving analysis computer can allocate a draftingreward based on the drafting property. For example, drafting reward(e.g., remunerative payment) can include at least one of a cash payment,a carbon credit, a fuel credit, a tax credit, a rebate, and at least aportion of a drafting fuel savings amount associated with the firstvehicle. The drafting amount can be allocated to an associated with thevehicle or vehicle owner to which the allocation is made. In addition,the reward can be allocated at regular intervals while the vehicles areengaged in the drafting relationship. For example, a reward can beallocated every second, minute, or mile in which the vehicles areengaged in the drafting relationship. The drafting reward can bedetermined according to rules or agreements established between driversand/or owners of the vehicles. The system can further be configured tocause the reward to be received by the party to which it is allocated.For example, if the reward is a payment, the system can cause thepayment to be debited from a bank account associated with the followingvehicle and credited to the account of the lead vehicle.

In various embodiments, the steps of 401-405 can be applied to a caravanof vehicles engaged in a drafting relationship. In an embodiment, afollowing vehicle allocates a reward to the vehicle it is following,which may in turn be following another vehicle. In such examples,rewards provided by a vehicle can be offset by reward received by thevehicle.

In various embodiments, the system can notify a vehicle engaged in adrafting relationship of a drafting characteristic, a drafting property,and/or drafting reward. In an embodiment, the notification can be sentto the owner of the vehicle or entity which is authorized to receive thenotifications. The notifications can be made in real-time. In addition,the notifications can be sent to, for example, a mobile phone or otherdevice associated with the vehicle, owner, or authorized party.

In some examples, certain functionality may be performed invehicle-based driving analysis computers 214 and 224, while otherfunctionality may be performed by the driving analysis computer 251 atthe driving analysis server 250. For instance, vehicle-based drivinganalysis computers 214 and 224 may continuously receive and analyzedriving data for their own vehicles 210 and 220 and nearby vehicles (viaV2V communications), and may determine driving characteristics (e.g.,autonomous driving characteristics, drafting characteristics, etc.) fortheir own vehicles 210 and 220 and/or the other nearby vehicles. Afterthe vehicle-based driving analysis computers 214 and 224 have determinedthe driving characteristics, indications of these characteristics may betransmitted to the server 250 so that the driving analysis computer 251can perform the insurance policy determinations and adjustments based onthe driving characteristics. Thus, the driving analysis server 250 maybe optional in certain embodiments, and some or all of the drivinganalyses may be performed within the vehicles themselves.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention. Inaddition, where reference has been made in this disclosure to items inthe figures, in some instances the alphabetic suffix (e.g., “A” or “B”)of a reference number has been omitted when it is desired to generallyreference (e.g., “226”) the item without specificity as to which of theplurality of items corresponding to the same base reference number.

What is claimed is:
 1. A driving analysis system comprising: a vehicle operational data collector, wherein the vehicle operational data collector is configured to collect vehicle operational data from at least one of vehicle sensors, vehicle on board diagnostic systems, and vehicle-to-vehicle communication systems associated with at least one of a first vehicle and a second vehicle; and a driving analysis computing device comprising: a processing unit comprising a processor; and a memory unit; and an electronic receiver, wherein the driving analysis computing device is configured to: receive, via the electronic receiver, the vehicle operational data; determine, by the processor and using the vehicle operational data, whether the first vehicle is engaged in a drafting relationship with at least the second vehicle, wherein the drafting relationship indicates that the first vehicle and the second vehicle move in alignment to reduce an effect of drag by using a slipstream of the second vehicle; determine, by the processor, a drafting characteristic of the drafting relationship using the vehicle operational data; determine, by the processor, a drafting property associated with the first vehicle using the drafting characteristic; and allocate, by the processor, to the second vehicle a drafting reward based on the drafting property associated with the first vehicle.
 2. The driving analysis system of claim 1, wherein the drafting reward comprises at least one of a cash payment, a carbon credit, a fuel credit, a tax credit, a rebate, and at least a portion of a drafting fuel savings amount associated with the first vehicle.
 3. The driving analysis system of claim 1, wherein the drafting characteristic comprises a vehicle spacing between the first vehicle and the second vehicle and a first vehicle speed, and the drafting property comprises a drafting fuel savings rate.
 4. The driving analysis system of claim 1, further comprising: a display wherein the driving analysis computing device is further configured to present at least one of the drafting property and the drafting reward on the display.
 5. The driving analysis system of claim 4, wherein the driving analysis computing device is further configured to display, in real-time, the drafting property, and wherein the drafting property comprises a percentage increase in miles-per-gallon of the first vehicle.
 6. The driving analysis system of claim 1, wherein the drafting property is a drafting fuel savings amount, and wherein the drafting fuel savings amount is determined by subtracting from a first drafting miles-per-gallon reading a first non-drafting miles-per-gallon reading to determine a first drafting miles-per-gallon savings rate and dividing a first distance traveled by the first vehicle by the first drafting miles-per-gallon savings rate.
 7. The driving analysis system of claim 1, wherein the drafting characteristic is a vehicle spacing between the first vehicle and the second vehicle, and the drafting property is a percent increase in miles-per-gallon of the first vehicle.
 8. The driving analysis system of claim 7, wherein the drafting property is determined from a correlation of vehicle spacing versus an increase in miles-per-gallon for the first vehicle.
 9. The driving analysis system of claim 8, wherein the correlation is based at least in part on a first vehicle type, a second vehicle type, a first vehicle speed, and a first vehicle miles-per-gallon at the first vehicle speed.
 10. The driving analysis system of claim 8, further comprising: a driving data database, wherein the correlation is stored on the driving data database and the driving analysis computing device is further configured to access the driving data database.
 11. The driving analysis system of claim 1, wherein the vehicle operational data is received from the second vehicle, and wherein the vehicle operational data is transmitted from the first vehicle to the second vehicle using the vehicle-to-vehicle communication systems.
 12. The driving analysis system of claim 1, wherein the vehicle operational data is collected by at least one of a first vehicle data acquiring component of the first vehicle and a second vehicle data acquiring component of the second vehicle.
 13. The driving analysis system of claim 1, wherein at least a portion of the vehicle operational data includes data collected via vehicle-to-vehicle communications between the first vehicle and the second vehicle.
 14. A driving analysis computing device comprising: a processing unit comprising a processor; a memory unit; and a wireless receiver, wherein the driving analysis computing device is configured to: receive, via the wireless receiver, driving data collected by at least one vehicle data acquiring component configured to collect the driving data from at least one of vehicle sensors, vehicle on-board diagnostics (OBD) systems, and vehicle-to-vehicle communication systems; determine, by the processor, a drafting property associated with a first vehicle engaged in a drafting relationship with a second vehicle, wherein the drafting relationship indicates that the first vehicle and the second vehicle move in alignment to reduce an effect of drag by using a slipstream of the second vehicle; and allocate, by the processor, a drafting reward to the second vehicle based on the drafting property associated with the first vehicle.
 15. The driving analysis computing device of claim 14, wherein the drafting property comprises a drafting fuel savings amount and the drafting reward comprises a payment equal to at least a portion of the drafting fuel savings amount associated with the first vehicle.
 16. The driving analysis computing device of claim 14, wherein the driving analysis computing device is further configured to assess, to an account associated with the first vehicle, a first payment.
 17. A method comprising: receiving, by a wireless receiver of a driving analysis computing device, vehicle operational data pertaining to at least one of a first vehicle and a second vehicle and collected by a vehicle operational data collector, wherein the vehicle operational data collector is configured to collect the vehicle operational data from at least one of vehicle sensors, vehicle on board diagnostic systems, and vehicle-to-vehicle communication systems; determining, by a processing unit of the driving analysis computing device, whether the first vehicle and the second vehicle are engaged in a drafting relationship, wherein the drafting relationship indicates that the first vehicle and the second vehicle move in alignment to reduce an effect of drag by using a slipstream of the second vehicle; determining, by the processing unit of the driving analysis computing device, a drafting characteristic of the drafting relationship using the vehicle operational data; determining, by the processing unit of the driving analysis computing device, a drafting property associated with the first vehicle using the drafting characteristic; and allocating, by the processing unit of the driving analysis computing device, a drafting reward to the second vehicle based on the drafting property associated with the first vehicle.
 18. The method of claim 17, wherein the first vehicle is following the second vehicle.
 19. The method of claim 17, wherein the drafting reward comprises at least one of a cash payment, carbon credit, a fuel credit, a tax credit, a rebate, and at least a portion of a drafting fuel savings amount associated with the first vehicle.
 20. The method of claim 17, wherein the drafting characteristic comprises a vehicle spacing between the first vehicle and the second vehicle and a first vehicle speed, and the drafting property comprises a drafting fuel savings rate.
 21. The method of claim 17, further comprising: displaying, on a computing device associated with the first vehicle, at least one of the drafting property and the drafting reward.
 22. The method of claim 21, wherein the drafting property comprises a percentage increase in miles-per-gallon of the first vehicle.
 23. The method of claim 22, wherein the drafting property is determined at least in part from a correlation of vehicle spacing versus an increase in miles-per-gallon for the first vehicle.
 24. The method of claim 23, wherein the correlation is based at least in part on a first vehicle type, a second vehicle type, a first vehicle speed, and a first vehicle miles-per-gallon at the first vehicle speed. 